Sample Info

single cell RNAseq

single end

non-stranded

50 bp reads

Drosophila

QA/QC

Coverage

Percentage of mitochondrial genes

GFP expression

Detected gene

count >= 1

Coverage vs Detected gene

Commonly detected gene

Expression level

expression of detected genes (count>=1)

Filter

Filter sample

Mapped reads >= 1 million

Percentage of mitochondrial genes <= 10%

Detected gene >= 4000

counts of GFP > 0

Filter gene

This is depends on how rare the sub-population we are looking for. Here we hope the population are more than 5 cells. Commonly expressed gene (count>=5) in cells >=5

Use protein coding gene only for downstream analysis.

ERCC correlation after filtering

Correlation after filtering

PCA after filtering

Normalization

use RUV package

Correlation after normalization

PCA after normalization

Marker gene expression

Expression table

ERCC fitting for selecting highly variable genes

p<1e-5, N=1599

PCA of highly variable genes

Clustering

use Seurat package

t-SNE before and after clustering

t-SNE using 7 PCs

Some marker genes

Top 10 marker genes of each cluster

DE genes

Trajectory analysis

R version 3.3.1 (2016-06-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets [8] methods base

other attached packages: [1] Seurat_1.4.0.6 cowplot_0.7.0
[3] statmod_1.4.26 genefilter_1.54.2
[5] DESeq_1.24.0 locfit_1.5-9.1
[7] RUVSeq_1.6.2 edgeR_3.14.0
[9] limma_3.28.21 EDASeq_2.6.2
[11] ShortRead_1.30.0 BiocParallel_1.6.6
[13] lattice_0.20-34 GenomicFeatures_1.24.5
[15] AnnotationDbi_1.34.4 GenomicAlignments_1.8.4
[17] Rsamtools_1.24.0 Biostrings_2.40.2
[19] XVector_0.12.1 SummarizedExperiment_1.2.3 [21] Biobase_2.32.0 GenomicRanges_1.24.3
[23] GenomeInfoDb_1.8.7 IRanges_2.6.1
[25] S4Vectors_0.10.3 BiocGenerics_0.18.0
[27] knitr_1.14 pander_0.6.0
[29] scales_0.4.1 pheatmap_1.0.8
[31] RColorBrewer_1.1-2 reshape2_1.4.2
[33] ggplot2_2.2.0 rmarkdown_1.1

loaded via a namespace (and not attached): [1] Rtsne_0.11 VGAM_1.0-2 minqa_1.2.4
[4] colorspace_1.2-7 hwriter_1.3.2 class_7.3-14
[7] modeltools_0.2-21 mclust_5.2 MatrixModels_0.4-1 [10] flexmix_2.3-13 mvtnorm_1.0-5 ranger_0.6.0
[13] codetools_0.2-15 splines_3.3.1 R.methodsS3_1.7.1
[16] mnormt_1.5-5 robustbase_0.92-6 geneplotter_1.50.0 [19] tclust_1.2-3 nloptr_1.0.4 caret_6.0-73
[22] pbkrtest_0.4-6 annotate_1.50.1 cluster_2.0.5
[25] kernlab_0.9-25 R.oo_1.21.0 assertthat_0.1
[28] Matrix_1.2-7.1 lazyeval_0.2.0 formatR_1.4
[31] lars_1.2 htmltools_0.3.5 quantreg_5.29
[34] tools_3.3.1 igraph_1.0.1 gtable_0.2.0
[37] dplyr_0.5.0 Rcpp_0.12.8 trimcluster_0.1-2
[40] gdata_2.17.0 ape_4.0 nlme_3.1-128
[43] rtracklayer_1.32.2 iterators_1.0.8 fpc_2.1-10
[46] stringr_1.1.0 lme4_1.1-12 irlba_2.1.2
[49] gtools_3.5.0 XML_3.98-1.4 DEoptimR_1.0-6
[52] zlibbioc_1.18.0 MASS_7.3-45 aroma.light_3.2.0
[55] SparseM_1.72 yaml_2.1.13 pbapply_1.3-1
[58] gridExtra_2.2.1 segmented_0.5-1.4 biomaRt_2.28.0
[61] fastICA_1.2-0 latticeExtra_0.6-28 stringi_1.1.2
[64] RSQLite_1.0.0 foreach_1.4.3 caTools_1.17.1
[67] boot_1.3-18 prabclus_2.2-6 matrixStats_0.51.0 [70] bitops_1.0-6 evaluate_0.10 ROCR_1.0-7
[73] labeling_0.3 R6_2.2.0 plyr_1.8.4
[76] magrittr_1.5 gplots_3.0.1 DBI_0.5-1
[79] sn_1.4-0 mgcv_1.8-16 mixtools_1.0.4
[82] survival_2.40-1 RCurl_1.96-0 nnet_7.3-12
[85] tibble_1.2 tsne_0.1-3 car_2.1-4
[88] KernSmooth_2.23-15 grid_3.3.1 FNN_1.1
[91] ModelMetrics_1.1.0 digest_0.6.10 diptest_0.75-7
[94] xtable_1.8-2 numDeriv_2016.8-1 R.utils_2.5.0
[97] munsell_0.4.3

Single cell RNAseq Demo

Xons

2017-01-18